3D Shape-Based Body Composition Inference Model Using a Bayesian Network
3D Shape-Based Body Composition Inference Model Using a Bayesian Network
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DOI：
10.1109/jbhi.2019.2903190
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发表时间：
2020-01-01
影响因子：
7.7
通讯作者：
Zhang, Xiaoke
中科院分区：
文献类型：
--
作者：
Lu, Yao;Hahn, James K.;Zhang, Xiaoke
身体成分可以通过许多不同的方式进行评估。高端医疗设备，如双能X射线吸收测定法（DXA）、计算机断层扫描（CT）和磁共振成像（MRI），提供高保真像素/体素级评估，但成本过高。在DXA和CT的情况下，该方法使用户暴露于电离辐射。全身空气置换体积描记法（BOD-POD）可以准确地估计身体密度，但评估仅限于全身脂肪百分比。光学三维（3D）扫描和重建技术，如使用深度相机，带来了新的机会，通过智能分析身体形状特征，以改善身体成分评估。在本文中，我们提出了一种新型的监督推理模型，利用3D几何特征和身体密度来预测像素级身体成分和体脂百分比。首先，我们使用身体密度来模拟脂肪分布基础预测。然后，我们使用贝叶斯网络来推断具有3D几何特征的基础预测偏差的概率。最后，我们使用非参数回归校正偏差。我们使用DXA评估作为模型训练和验证的基础事实。我们比较我们的方法，在像素级的身体成分评估方面，与当前最先进的预测模型。我们的方法比那些预测模型平均高出52.69。我们还比较了我们的方法，在全身脂肪百分比评估方面，与医疗级设备BOD POD。我们的方法优于BOD POD 23.28。
Body composition can be assessed in many different ways. High-end medical equipment, such as Dual-energy X-ray Absorptiometry (DXA), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) offers high-fidelity pixel/voxel-level assessment, but is prohibitive in cost. In the case of DXA and CT, the approach exposes users to ionizing radiation. Whole-body air displacement plethysmography (BOD POD) can accurately estimate body density, but the assessment is limited to the whole-body fat percentage. Optical three-dimensional (3D) scan and reconstruction techniques, such as using depth cameras, have brought new opportunities for improving body composition assessment by intelligently analyzing body shape features. In this paper, we present a novel supervised inference model to predict pixel-level body composition and percentage of body fat using 3D geometry features and body density. First, we use body density to model a fat distribution base prediction. Then, we use a Bayesian network to infer the probability of the base prediction bias with 3D geometry features. Finally, we correct the bias using non-parametric regression. We use DXA assessment as the ground truth in model training and validation. We compare our method, in terms of pixel-level body composition assessment, with the current state-of-the-art prediction models. Our method outperforms those prediction models by 52.69 on average. We also compare our method, in terms of whole-body fat percentage assessment, with the medical-level equipment-BOD POD. Our method outperforms the BOD POD by 23.28.